Image-Based Subthalamic Nucleus Segmentation for Deep Brain Surgery with Electrophysiology Aided Refinement

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00023752%3A_____%2F20%3A43920430" target="_blank" >RIV/00023752:_____/20:43920430 - isvavai.cz</a>

Alternative codes found

RIV/68407700:21230/20:00345805

Result on the web

<a href="https://link.springer.com/chapter/10.1007%2F978-3-030-60946-7_4" target="_blank" >https://link.springer.com/chapter/10.1007%2F978-3-030-60946-7_4</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-60946-7_4" target="_blank" >10.1007/978-3-030-60946-7_4</a>

Result language

angličtina

Original language name

Image-Based Subthalamic Nucleus Segmentation for Deep Brain Surgery with Electrophysiology Aided Refinement

Original language description

Identification of subcortical structures is an essential step in surgical planning for interventions such as the deep brain stimulation (DBS), in which permanent electrode is implanted in a precisely defined location. For refinement of the target localisation and compensation of brain shift occurring during the surgery, intra-operative electrophysiological recording using microelectrodes is usually undertaken. In this paper, we present a multimodal method that consists of a) subthalamic nucleus (STN) segmentation from magnetic resonance T2 images using 3D active contour fitting and b) a subsequent brain shift compensation step, increasing the accuracy of microelectrode placement localisation by the probabilistic electrophysiology-based fitting. The method is evaluated on a data set of 39 multi-electrode trajectories from 20 patients undergoing DBS surgery for Parkinson’s disease in a leave-one-subject-out scenario. The performance comparison shows increased sensitivity and slightly decreased specificity of STN identification using the individually-segmented 3D contours, compared to electrophysiology-based refinement of a standard 3D atlas. To achieve accurate segmentation from the low-resolution clinical T2 images, a more sophisticated approach, including shape priors and intensity model, needs to be implemented. However, the presented approach is a step towards automatic identification of microelectrode recording sites and possibly also an assistive system for the DBS surgery. © 2020, Springer Nature Switzerland AG.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

20602 - Medical laboratory technology (including laboratory samples analysis; diagnostic technologies) (Biomaterials to be 2.9 [physical characteristics of living material as related to medical implants, devices, sensors])

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Publication year

2020

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

10th International Workshop on Multimodal Learning for Clinical Decision Support and 9th International Workshop on Clinical Image-Based Procedures

ISBN

978-3-030-60945-0

ISSN

0302-9743

e-ISSN

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Number of pages

10

Pages from-to

34-43

Publisher name

Springer

Place of publication

Berlin

Event location

Lima; Peru

Event date

Oct 4, 2020

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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